Climate change, as one of the most pressing challenges of the 21 century, has profoundly affected marine and coastal ecosystems, particularly coastal drylands. Rising sea surface temperatures , the increased frequency of extreme climate events, and intensified thermal stresses are among the key consequences of this global phenomenon. In this context, the application of downscaling numerical models such as the WRF model offers a novel approach for SST modeling in these environmentally sensitive regions. The Persian Gulf, recognized as one of the world's most significant coastal dryland ecosystems, is characterized by high annual evaporation, elevated salinity, and strong seasonal temperature fluctuations, rendering it particularly vulnerable to climate change. The primary objective of this study is to identify the most optimal configuration of physical parameterization schemes within the WRF model for simulating SST in the Persian Gulf. The WRF model was executed using eight distinct configurations, and the model outputs were validated using the RMSE, MAE, and MAPE. The results indicated that Configuration No. 5, which comprises the WSM 5-class microphysics, RRTM longwave radiation, Dudhia shortwave radiation, the Revised MM5 Monin Obukhov surface layer scheme, the Unified Noah land surface model, the YSU planetary boundary layer scheme, and the Kain–Fritsch cumulus parameterization scheme, exhibited the lowest error metrics and the highest correlation coefficient (0.985), and was therefore identified as the optimal configuration. The RMSE values for spring, summer, autumn, and winter were calculated as 0.036°C, 0.012°C, 0.028°C, and 0.011°C, respectively. Based on these findings, the WRF model outputs can serve as a suitable, high-precision alternative to the Bushehr meteorological buoy data for a range of applications, including climate change monitoring, fisheries management, the protection of coral reefs against bleaching induced by global warming, and the prediction of extreme temperature events in this coastal dryland region.